1. Technical Field
The present invention relates generally to sharpness enhancement of video images, and more specifically relates to a system and method of calculating a cost function to identify an optimal level of enhancement.
2. Related Art
Many video enhancement techniques exist which modify picture content in such a way that the resulting picture is improved. Improvements may be due to the attenuation of certain artifacts in the picture, due to the accentuation of certain information in the picture, or even due to the addition of new video data. One example of video enhancement is the noise reduction function, in which unwanted data is filtered out of the picture to improve the end picture quality. Another example of video enhancement is sharpness improvement, where existing high frequencies are selectively enhanced or new high frequencies are added to the picture to improve the perceived sharpness.
However, since it is not unusual for the same picture data to pass through a cascade of video enhancement algorithms, it is far from trivial to guarantee optimal picture quality. As a simplified example, consider the cascading of noise reduction and sharpness enhancement algorithms. The noise reduction algorithm might reduce the high frequencies in the picture that seem random—considering them as noise, whereas the sharpness enhancement function might boost those frequencies—considering them as texture. Although one might expect the noise reduction algorithm to do a good job on noise reduction, it is not necessarily producing a noise-free picture. And as such, the sharpness enhancement algorithm needs to take the ‘left-over’ noise into account. In short, the functions used to enhance pictures are complex in nature, operate on the same picture data, and are interdependent. Thus, great care needs to be taken while designing and concatenating various video enhancement functions.
Similar issued are faced when designing just the sharpness enhancement system, since it is often desirous to implement more than sharpness enhancement function. Namely, in systems that utilize a plurality of sharpness enhancement functions, the interdependence of the functions may result in the same types of problems described above.
Sharpness enhancement functions may, for example, modify the gradient of the edges to create sharpness enhancement, often referred to as ‘Luminance Transient Improvement’ (LTI). Adding overshoots and undershoots near the gradients is another way of improving the sharpness impression (i.e., the “Mach Band” effect). This is done by selectively boosting mid-band and/or high-band frequencies in the picture, and is referred to as “peaking.”
FIG. 1 depicts an implementation of a sharpness enhancement system 10 that enhances an original picture 12 using a pair of algorithms or enhancement functions 16 (e.g., peaking and LTI). The enhanced picture output 14 is defined by:Fenh({right arrow over (x)},n)=F({right arrow over (x)},n)+α*Fenh1({right arrow over (x)},n)+β*Fenh2({right arrow over (x)},n)  (1)where F({right arrow over (x)},n) is the input video signal,       x    →    =      (                            x                                      y                      )  is the position, n the picture number, Fenh1 is the output of enhancement 1, e.g., peaking and Fenh2 is the output of enhancement 2, e.g., LTI. The coefficients α and β are defined as the “enhancement vector.”
In conventional methods, based upon the characteristics of the input signal F({right arrow over (x)},n), the coefficients α and β are selected independently while designing each of the enhancement functions 16. The parameters are often experimentally optimized to arrive at a reasonable setting in the combined scheme shown in FIG. 1. This is a critical task and can be quite tedious if the system gets larger. Also, since coefficients α and β are assumed to be independent, the interdependency is not seen in the function explicitly, which can cause artifacts if thorough testing is not performed.
Another drawback of this system is that it is not easy to expand or modify the set of functions 16 since the task of ‘manual’ optimization needs to be repeated. Also, spatial and temporal consistency in the functions 16 is not accounted for. Even if during the design of the individual algorithms, care is taken for consistency, the combination of algorithms could change this.
Accordingly, a need exists for a system and method of performing video enhancement that is completely expandable, takes into account the interdependencies of various functions, and guarantees spatial and temporal consistency.